Distributed Tomographic Reconstruction with Quantization

Conventional tomographic reconstruction typically depends on centralized servers for both data storage and computation, leading to concerns about memory limitations and data privacy. Distributed reconstruction algorithms mitigate these issues by partitioning data across multiple nodes, reducing serv...

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Bibliographic Details
Published in:arXiv.org 2024-10
Main Authors: Miao, Runxuan, Aslan, Selin, Koyuncu, Erdem, Doğa Gürsoy
Format: Article
Language:English
Subjects:
Algorithms
Cluster analysis
Clustering
Data storage
Distributed memory
Image compression
Image enhancement
Image reconstruction
Nodes
Privacy
Vector quantization
Online Access:Get full text
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Summary:Conventional tomographic reconstruction typically depends on centralized servers for both data storage and computation, leading to concerns about memory limitations and data privacy. Distributed reconstruction algorithms mitigate these issues by partitioning data across multiple nodes, reducing server load and enhancing privacy. However, these algorithms often encounter challenges related to memory constraints and communication overhead between nodes. In this paper, we introduce a decentralized Alternating Directions Method of Multipliers (ADMM) with configurable quantization. By distributing local objectives across nodes, our approach is highly scalable and can efficiently reconstruct images while adapting to available resources. To overcome communication bottlenecks, we propose two quantization techniques based on K-means clustering and JPEG compression. Numerical experiments with benchmark images illustrate the tradeoffs between communication efficiency, memory use, and reconstruction accuracy.
ISSN:2331-8422